Clockwork Hierarchal Variational Encoder

ABSTRACT

A method for providing a frame-based mel spectral representation of speech includes receiving a text utterance having at least one word, and selecting a mel spectral embedding for the text utterance. Each word in the text utterance has at least one syllable and each syllable has at least one phoneme. For each phoneme, using the selected mel spectral embedding, the method also includes: predicting a duration of the corresponding phoneme by encoding linguistic features of the corresponding phoneme with a corresponding syllable embedding for the syllable that includes the corresponding phoneme; and generating a plurality of fixed-length predicted mel-frequency spectrogram frames based on the predicted duration for the corresponding phoneme. Each fixed-length predicted mel-frequency spectrogram frame representing mel-spectral information of the corresponding phoneme.

CROSS REFERENCE TO RELATED APPLICATIONS

This U.S. patent application is a continuation of, and claims priority under 35 U.S.C. § 120 from, U.S. patent application Ser. No. 16/678,981, filed on Nov. 8, 2019, which is a Continuation-In-Part of, and claims priority under 35 U.S.C. § 120 from, U.S. patent application Ser. No. 16/382,722, filed on Apr. 12, 2019, which claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application 62/670,384, filed on May 11, 2018. The disclosure of these prior applications are considered part of the disclosure of this application and are hereby incorporated by reference in their entireties.

TECHNICAL FIELD

This disclosure relates to a clockwork hierarchal variational encoder for predicting prosody.

BACKGROUND

Speech synthesis systems use text-to-speech (TTS) models to generate speech from textual input. The generated/synthesized speech should accurately convey the message (intelligibility) while sounding like human speech (naturalness) with an intended prosody (expressiveness). While traditional concatenative and parametric synthesis models are capable of providing intelligible speech and recent advances in neural modeling of speech have significantly improved the naturalness of synthesized speech, most existing TTS models are ineffective at modeling prosody, thereby causing synthesized speech used by important applications to lack expressiveness. For instance, it is desirable for applications such as conversational assistants and long-form readers to produce realistic speech by imputing prosody features not conveyed in textual input, such as intonation, stress, and rhythm and style. For example, a simple statement can be spoken in many different ways depending on whether the statement is a question, an answer to a question, there is uncertainty in the statement, or to convey any other meaning about the environment or context which is unspecified by the input text.

SUMMARY

One aspect of the disclosure provides a method of providing a frame-based mel spectral representation of speech. The method includes receiving, at data processing hardware, a text utterance having at least one word, and selecting, by the data processing hardware, a mel spectral embedding for the text utterance. Each word in the text utterance has at least one syllable and each syllable has at least one phoneme. For each phoneme, using the selected mel spectral embedding, the method also includes: predicting a duration of the corresponding phoneme by encoding linguistic features of the corresponding phoneme with a corresponding syllable embedding for the syllable that includes the corresponding phoneme; and generating a plurality of fixed-length predicted mel-frequency spectrogram frames based on the predicted duration for the corresponding phoneme. Each fixed-length predicted mel-frequency spectrogram frame representing mel-spectral information of the corresponding phoneme.

Implementations of the disclosure may include one or more of the following optional features. In some implementations, a network representing a hierarchical linguistic structure of the text utterance includes a first level including each syllable of the text utterance, a second level including each phoneme of the text utterance, and a third level including each fixed-length predicted mel-frequency spectrogram frame. In these implementations, the first level of the network may include a long short-term memory (LSTM) processing cell representing each syllable of the text utterance, the second level of the network may include a LSTM processing cell representing each phoneme of the text utterance, and the third level of the network may include a LSTM processing cell representing each fixed-length predicted mel-frequency spectrogram frame. Here, the LSTM processing cells of the second level clock relative to and faster than the LSTM processing cells of the first level, while the LSTM processing cells of the third level clock relative to and faster than the LSTM processing cells of the second level.

In some examples, the method also includes converting, by the data processing hardware, using a vocoder network, the plurality of generated fixed-length predicted mel-frequency spectrogram frames into a time-domain audio waveform. The vocoder network may be conditioned on mel-frequency spectrograms for conversion into time-domain audio waveforms. Additionally, the vocoder network and a deep neural network representing a hierarchical linguistic structure of the text utterance may be trained separately.

In some implementations, the method also includes, for each phoneme, using the selected mel spectral embedding: predicting, by the data processing hardware, an energy contour of the corresponding phoneme based on the predicted duration for the phoneme; and generating, by the data processing hardware, a plurality of fixed-length predicted energy frames based on the predicted duration for the corresponding phoneme. In these implementations, each fixed-length energy frame represents the predicted energy contour of the corresponding phoneme. A hierarchical linguistic structure representing the text utterance may include: a first level including each syllable of the text utterance; a second level including each phoneme of the text utterance; a third level including each fixed-length predicted mel-frequency spectrogram frame; and a fourth level parallel to the third level and including each fixed-length predicted energy frame for each phoneme of the text utterance.

In additional implementations, the method also includes, for each syllable, using the selected mel spectral embedding: predicting, by the data processing hardware, a duration of the syllable by summing the predicted durations for each phoneme associated with the syllable; predicting, by the data processing hardware, a pitch contour of the syllable based on the predicted duration for the syllable; and generating, by the data processing hardware, a plurality of fixed-length predicted pitch frames based on the predicted duration of the syllable. Each fixed-length predicted pitch frame represents part of the predicted pitch contour of the syllable. A hierarchical linguistic structure representing the text utterance may include: a first level including each syllable of the text utterance; a second level including each phoneme of the text utterance; a third level including each fixed-length predicted mel-frequency spectrogram frame; and a fourth level parallel to the third level and including each fixed-length predicted pitch frame for each syllable of the text utterance.

The mel spectral embedding may include a fixed-length numerical vector. In some examples, the method also includes receiving, at the data processing hardware, training data including a plurality of reference audio signals, each reference audio signal comprising a spoken utterance of human speech and having a corresponding prosody. In these examples, the method also includes training, by the data processing hardware, a deep neural network for a mel-frequency spectrogram model by: encoding each reference audio signal into a corresponding fixed-length utterance embedding representing the corresponding prosody of the reference audio signal; and decoding each corresponding fixed-length utterance embedding into a plurality of corresponding fixed-length mel-frequency spectrogram frames representing the corresponding prosody of the reference audio signal.

Another aspect of the disclosure provides a system for providing a frame-based mel spectral representation of speech. The system includes data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that when executed on the data processing hardware cause the data processing hardware to perform operations. The operations include receiving a text utterance having at least one word and selecting a mel spectral embedding for the text utterance. Each word in the text utterance has at least one syllable and each syllable has at least one phoneme. For each phoneme, using the selected mel spectral embedding, the operations also include: predicting a duration of the corresponding phoneme by encoding linguistic features of the corresponding phoneme with a corresponding syllable embedding for the syllable that includes the corresponding phoneme; and generating a plurality of fixed-length predicted mel-frequency spectrogram frames based on the predicted duration for the corresponding phoneme. Each fixed-length predicted mel-frequency spectrogram frame representing mel-spectral information of the corresponding phoneme.

This aspect may include one or more of the following optional features. In some implementations, a network representing a hierarchical linguistic structure of the text utterance includes a first level including each syllable of the text utterance, a second level including each phoneme of the text utterance, and a third level including each fixed-length predicted mel-frequency spectrogram frame. In these implementations, the first level of the network may include a long short-term memory (LSTM) processing cell representing each syllable of the text utterance, the second level of the network may include a LSTM processing cell representing each phoneme of the text utterance, and the third level of the network may include a LSTM processing cell representing each fixed-length predicted mel-frequency spectrogram frame. Here, the LSTM processing cells of the second level clock relative to and faster than the LSTM processing cells of the first level, while the LSTM processing cells of the third level clock relative to and faster than the LSTM processing cells of the second level.

In some examples, the operations also include converting, using a vocoder network, the plurality of generated fixed-length predicted mel-frequency spectrogram frames into a time-domain audio waveform. The vocoder network may be conditioned on mel-frequency spectrograms for conversion into time-domain audio waveforms. Additionally, the vocoder network and a deep neural network representing a hierarchical linguistic structure of the text utterance may be trained separately.

In some implementations, the operations also include, for each phoneme, using the selected mel spectral embedding: predicting an energy contour of the corresponding phoneme based on the predicted duration for the phoneme; and generating a plurality of fixed-length predicted energy frames based on the predicted duration for the corresponding phoneme. In these implementations, each fixed-length energy frame represents the predicted energy contour of the corresponding phoneme. A hierarchical linguistic structure representing the text utterance may include: a first level including each syllable of the text utterance; a second level including each phoneme of the text utterance; a third level including each fixed-length predicted mel-frequency spectrogram frame; and a fourth level parallel to the third level and including each fixed-length predicted energy frame for each phoneme of the text utterance.

In additional implementations, the operations also include, for each syllable, using the selected mel spectral embedding: predicting a duration of the syllable by summing the predicted durations for each phoneme associated with the syllable; predicting a pitch contour of the syllable based on the predicted duration for the syllable; and generating a plurality of fixed-length predicted pitch frames based on the predicted duration of the syllable. Each fixed-length predicted pitch frame represents part of the predicted pitch contour of the syllable. A hierarchical linguistic structure representing the text utterance may include: a first level including each syllable of the text utterance; a second level including each phoneme of the text utterance; a third level including each fixed-length predicted mel-frequency spectrogram frame; and a fourth level parallel to the third level and including each fixed-length predicted pitch frame for each syllable of the text utterance.

The mel spectral embedding may include a fixed-length numerical vector. In some examples, the operations also include receiving training data including a plurality of reference audio signals, each reference audio signal comprising a spoken utterance of human speech and having a corresponding prosody. In these examples, the operations also include training a deep neural network for a mel-frequency spectrogram model by: encoding each reference audio signal into a corresponding fixed-length utterance embedding representing the corresponding prosody of the reference audio signal; and decoding each corresponding fixed-length utterance embedding into a plurality of corresponding fixed-length mel-frequency spectrogram frames representing the corresponding prosody of the reference audio signal.

The details of one or more implementations of the disclosure are set forth in the accompanying drawings and the description below. Other aspects, features, and advantages will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic view of an example system for training a deep neural network to provide a controllable prosody model for use in predicting a prosodic representation for a text utterance.

FIG. 2A is a schematic view of a hierarchical linguistic structure for encoding prosody of a reference audio signal into a fixed-length utterance embedding.

FIG. 2B is a schematic view of a hierarchical linguistic structure using a fixed-length utterance embedding to predict a prosodic representation of a text utterance.

FIG. 2C is a schematic view of an encoder portion of a hierarchical linguistic structure configured to encode fixed-length reference frames directly into a fixed-length utterance embedding.

FIGS. 3A and 3B are schematic views of an example autoencoder for predicting duration and pitch contours for each syllable of a text utterance.

FIG. 3C is a schematic view of an example autoencoder for predicting duration and energy contours for each phoneme of a text utterance.

FIG. 4 is a flowchart of an example arrangement of operations for a method of predicting a prosodic representation of a received text utterance.

FIGS. 5A and 5B are schematic views of an example autoencoder for predicting a mel-frequency spectrogram a text utterance.

FIG. 6 is a flowchart of an example arrangement of operations for a method of predicting a mel-frequency spectrogram for a text utterance.

FIG. 7 is a schematic view of an example computing device that may be used to implement the systems and methods described herein.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Text-to-speech (TTS) models, often used by speech synthesis systems, are generally only given text inputs without any reference acoustic representation at runtime, and must impute many linguistic factors that are not provided by the text inputs in order to produce realistically sounding synthesized speech. A subset of these linguistic factors are collectively referred to as prosody and may include intonation (pitch variation), stress (stressed syllables vs. non-stressed syllables), duration of sounds, loudness, tone, rhythm, and style of the speech. Prosody may indicate the emotional state of the speech, the form of the speech (e.g., statement, question, command, etc.), the presence of irony or sarcasm of the speech, uncertainty in the knowledge of the speech, or other linguistic elements incapable of being encoded by grammar or vocabulary choice of the input text. Accordingly, a given text input that is associated with a high degree of prosodic variation can produce synthesized speech with local changes in pitch and speaking duration to convey different semantic meanings, and also with global changes in the overall pitch trajectory to convey different moods and emotions.

Neural network models provide potential for robustly synthesizing speech by predicting linguistic factors corresponding to prosody that are not provided by text inputs. As a result, a number of applications, such as audiobook narration, news readers, voice design software, and conversational assistants can produce realistically sounding synthesized speech that is not monotonous-sounding. Implementations herein are directed toward a neural network model that includes a variational autoencoder (VAE) having an encoder portion for encoding a reference audio signal corresponding to a spoken utterance into an utterance embedding that represents the prosody of the spoken utterance, and a decoder portion that decodes the utterance embedding to predict durations of phonemes and pitch and energy contours for each syllable.

The encoder portion may train utterance embeddings representing prosody by encoding numerous reference audio signals conditioned on linguistic features representing the utterances. The linguistic features may include, without limitation, individual sounds for each phoneme, whether each syllable is stressed or un-stressed, the type of each word (e.g., noun/adjective/verb) and/or the position of the word in the utterance, and whether the utterance is a question or phrase. Each utterance embedding is represented by a fixed-length numerical vector. In some implementations, the fixed-length numerical vector includes a value equal to 256. However, other implementations may use fixed-length numerical vectors having values greater than or less than 256. The decoder portion may decode a fixed-length utterance embedding into a sequence of phoneme durations via a first decoder and into a sequence of fixed-length frames (e.g., five millisecond) of pitch and energy using the phoneme durations. During training, the phoneme durations and fixed-length frames of pitch and energy predicted by the decoder portion closely match the phoneme durations and fixed-length frames of pitch and energy sampled from the reference audio signal associated with the fixed-length utterance embedding.

The VAE of the present disclosure includes a Clockwork Hierarchal Variational Autoencoder (CHiVE) that incorporates hierarchical stacked layers of long-short term-memory (LSTM) cells, with each layer of LSTM cells incorporating structure of the utterance such that one layer represents phonemes, a next layer represents syllables, and another layer represents words. Moreover, the hierarchy of stacked layers of LSTM cells are variably clocked to a length of hierarchical input data. For instance, if the input data contains a word of three syllables followed by a word of four syllables, then the syllable layer of the CHiVE would clock three times relative to a single clock of the word layer for the first input word, and then the syllable layer would clock four more times relative to a subsequent single clock of the word layer for the second word. Thus, rather than using frame-based techniques where memory associated with given LSTM cell is only effective for about a half second (i.e., 100 times steps with a five (5) millisecond frame rate), and thus, only providing the LSTM cell memory for two or three syllables of speech, the phoneme, word, and syllable layers of the CHiVE clock with phonemes, words, and syllables, respectively, giving the LSTM cells of the stacked layers memory over the last 100 words, syllables, or phonemes.

During inference, the CHiVE is configured to receive a text utterance and select an utterance embedding for the text utterance. The received text utterance has at least one word, each word has at least one syllable, and each syllable has at least one phoneme. Since the text utterance is missing context, semantic information, and pragmatic information to guide the appropriate prosody for producing synthesized speech from the utterance, the CHiVE uses that selected utterance embedding as the latent variable to represent an intended prosody. Thereafter, the CHiVE uses the selected utterance embedding to predict a duration of each syllable by encoding linguistic features of each phoneme contained in the syllable with a corresponding prosodic syllable embedding for the syllable, and predict a pitch of each syllable based on the predicted duration for the syllable. Lastly, the CHiVE is configured to generate a plurality of fixed-length pitch frames based on the predicted duration for each syllable such that each fixed-length pitch frame represents the predicted pitch of the syllable. The CHiVE may similarly predict energy (e.g., loudness) of each syllable based on the predicted duration for the syllable and generate a plurality of fixed-length energy frames each representing the predicted energy of the syllable. The fixed-length pitch and/or energy frames may be provided to a unit-selection model or wave-net model of a TTS system to produce the synthesized speech with the intended prosody provided by the input fixed-length utterance embedding.

FIG. 1 shows an example system 100 for training a deep neural network 200 to provide a controllable prosody model 300, and for predicting a prosodic representation 322 for a text utterance 320 using the prosody model 300. The system 100 includes a computing system 120 having data processing hardware 122 and memory hardware 124 in communication with the data processing hardware 122 and storing instructions that cause the data processing hardware 122 to perform operations. In some implementations, the computing system 120 (e.g., the data processing hardware 122) provides a prosody model 300 based on a trained deep neural network 200 to a text-to-speech (TTS) system 150 for controlling prosody of synthesized speech 152 from an input text utterance 320. Since the input text utterance 320 has no way of conveying context, semantics, and pragmatics to guide the appropriate prosody of the synthesized speech 152, the prosody model 300 may predict a prosodic representation 322 for the input text utterance 320 by conditioning the model 300 on linguistic features extracted from the text utterance 320 and using a fixed-length utterance embedding 260 as a latent variable representing an intended prosody for the text utterance 320. In some examples, the computing system 120 implements the TTS system 150. In other examples, the computing system 120 and the TTS system 150 are distinct and physically separate from one another. The computing system may include a distributed system (e.g., cloud computing environment).

In some implementations, the deep neural network 200 is trained on a large set of reference audio signals 222. Each reference audio signal 222 may include a spoken utterance of human speech recorded by a microphone and having a prosodic representation. During training, the deep neural network 200 may receive multiple reference audio signals 222 for a same spoken utterance, but with varying prosodies (i.e., the same utterance can be spoken in multiple different ways). Here, the reference audio signals 222 are of variable-length such that the duration of the spoken utterances varies even though the content is the same. The deep neural network 200 is configured to encode/compress the prosodic representation associated with each reference audio signal 222 into a corresponding fixed-length utterance embedding 260. The deep neural network 200 may store each fixed-length utterance embedding 260 in an utterance embedding storage 180 (e.g., on the memory hardware 124 of the computing system 120) along with a corresponding transcript 261 of the reference audio signal 222 associated the utterance embedding 260. The deep neural network 200 may be further trained by back-propagating the fixed-length utterance embeddings 260 conditioned upon linguistic features extracted from the transcripts 261 to generate fixed-length frames of pitch, energy, and duration of each syllable.

During inference, the computing system 120 may use the prosody model 300 to predict a prosodic representation 322 for a text utterance 320. The prosody model 300 may select an utterance embedding 260 for the text utterance 320. The utterance embedding 260 represents an intended prosody of the text utterance 320. Described in greater detail below with reference to FIGS. 2A-2C and 3A-3C, the prosody model 300 may predict the prosodic representation 322 for the text utterance 320 using the selected utterance embedding 260. The prosodic representation 322 may include predicted pitch, predicted timing, and predicted loudness (e.g., energy) for the text utterance 320. In the example shown, the TTS system 150 uses the prosodic representation 322 to produce synthesized speech 152 from the text utterance 320 and having the intended prosody.

FIGS. 2A and 2B show a hierarchical linguistic structure (e.g., deep neural network of FIG. 1) 200 for a clockwork hierarchal variational autoencoder (CHiVE) 300 (′ autoencoder 300′) that provides a controllable model of prosody that jointly predicts, for each syllable of given input text, a duration of all phonemes in the syllable and pitch (F0) and energy (C0) contours for the syllable without relying on any unique mappings from the given input text or other linguistic specification to produce synthesized speech 152 having an intended/selected prosody. The autoencoder 300 includes an encoder portion 302 (FIG. 2A) that encodes a plurality of fixed-length reference frames 220 sampled from a reference audio signal 222 into a fixed-length utterance embedding 260, and a decoder portion 310 (FIG. 2B) that learns how to decode the fixed-length utterance embedding 260 into a plurality of fixed-length predicted frames 280. As will become apparent, the autoencoder 300 is trained so that the number of predicted frames 280 output from the decoder portion 310 is equal to the number of reference frames 220 input to the encoder portion 302. Moreover, the autoencoder 300 is trained so that data associated with the reference and predicted frames 220, 280 substantially match one another.

Referring to FIG. 2A, the encoder portion 302 receives the sequence of fixed-length reference frames 220 from the input reference audio signal 222. The input reference audio signal 222 may include a spoken utterance of human speech recorded by a microphone that includes a target prosody. The encoder portion 302 may receive multiple reference audio signals 222 for a same spoken utterance, but with varying prosodies (i.e., the same utterance can be spoken in multiple different ways). For example, the same spoken utterance may vary in prosody when the spoken reference is an answer to a question compared to when the spoken utterance is a question. The reference frames 220 may each include a duration of 5 milliseconds (ms) and represent one of a contour of pitch (F0) or a contour of energy (C0) for the reference audio signal 222. In parallel, the encoder portion 302 may also receive a second sequence of reference frames 220 each including a duration of 5 ms and representing the other one of the contour of pitch (F0) or the contour of energy (C0) for the reference audio signal 222. Accordingly, the sequence reference frames 220 sampled from the reference audio signal 222 provide a duration, pitch contour, and/or energy contour to represent prosody for the reference audio signal 222. The length or duration of the reference audio signal 222 correlates to a sum of the total number of reference frames 220.

The encoder portion 302 includes hierarchical levels of reference frames 220, phonemes 230,230 a, syllables 240, 240 a, and words 250, 250 a for the reference audio signal 222 that clock relative to one another. For instance, the level associated with the sequence of reference frames 220 clocks faster than the next level associated with the sequence of phonemes 230. Similarly, the level associated with the sequence of syllables 240 clocks slower than the level associated with the sequence of phonemes 230 and faster than the level associated with the sequence of words 250. Accordingly, the slower clocking layers receive, as input, an output from faster clocking layers so that the output after the final clock (i.e., state) of a faster layer is taken as the input to the corresponding slower layer to essentially provide a sequence-to-sequence encoder. In the examples shown, the hierarchical levels include Long Short-Term Memory (LSTM) levels.

In some examples, the encoder portion 302 first encodes the sequence of reference frames 220 into the sequence of phonemes 230. Each phoneme 230 receives, as input, a corresponding encoding of a subset of reference frames 220 and includes a duration equal to the number of reference frames 220 in the encoded subset. In the example shown, the first four fixed-length reference frames 220 are encoded into phoneme 230Aa1; the next three fixed-length reference frames 220 are encoded into phoneme 230Aa2; the next four fixed-length reference frames 220 are encoded into phoneme 230Ab1; the next two fixed-length reference frames 220 are encoded into phoneme 230Ba1, the next five fixed-length reference frames 220 are encoded into phoneme 230Ba2; the next four fixed-length reference frames 220 are encoded into phoneme 230Ba3; the next three fixed-length reference frames 220 are encoded into phoneme 230Ca1; the next four fixed-length reference frames 220 are encoded into phoneme 230Cb1; and the final two fixed-length reference frames 220 are encoded into phoneme 230Cb2. Thus, each phoneme 230 in the sequence of phonemes 230 includes a corresponding duration based on the number of reference frames 220 encoded into the phoneme 230 and corresponding pitch and/or energy contours. For instance, phoneme 230Aa1 includes a duration equal to 20 ms (i.e., four reference frames 220 each having the fixed-length of five milliseconds) and phoneme 230Aa2 includes a duration equal to 15 ms (i.e., three reference frames 220 each having the fixed-length of five milliseconds). Thus, the level of reference frames 220 clocks a total of seven times for a single clocking between the phoneme 230Aa1 and the next phoneme 230Aa2 for the level of phonemes 230.

After encoding the fixed-length frames 220 into corresponding phonemes 230, the encoder portion 302 is further configured to encode the sequence of phonemes 230 into the sequence of syllables 240 for the reference audio signal 222. Here, each syllable 240 receives, as input, a corresponding encoding of one or more phonemes 230 and includes a duration equal to a sum of the durations for the one or more phonemes 230 of the corresponding encoding. The duration of the syllables 240 may indicate timing of the syllables 240 and pauses in between adjacent syllables 240. In the example shown, the first two phonemes 230Aa1, 230Aa2 are encoded into syllable 240Aa; the next phoneme 230Ab1 is encoded into syllable 240Ab; each of phonemes 230Ba1, 230Ba2, 230Ba3 are encoded into syllable 240Ba; phoneme 230Ca1 is encoded into syllable 240Ca; and phonemes 230Cb1, 230Cb2 are encoded into syllable 240Cb. Each syllable 240Aa-240Cb in the level of syllables 240 may correspond to a respective syllable embedding (e.g., a numerical vector) that indicates a duration, pitch (F0), and/or energy (C0) associated with the corresponding syllable 240. Moreover, each syllable is indicative of a corresponding state for the level of syllables 240. For instance, syllable 240Aa includes a duration equal to 35 ms (i.e., the sum of the 20 ms duration for phoneme 230Aa1 and the 15 ms duration for phone 230A2) and syllable 240Ab includes a duration equal to 20 ms (i.e., the 20 ms duration for phoneme 230Ab1). Thus, the level of reference frames 220 clocks a total of eleven times and the level of phonemes 230 clocks a total of three times for a single clocking between the syllable 240Aa and the next syllable 240Ab for the level of syllables 240.

With continued reference to FIG. 2A, in some implementations, the encoder portion 302 further encodes the sequence of syllables 240 into the sequence of words 250 for the reference audio signal 222. Here, syllables 240Aa, 240Ab are encoded into word 250A; syllable 240Ba is encoded into word 250B; and syllables 240Ca, 240Cb are encoded into word 250C. Finally, the encoder portion 302 encodes the sequence of words 250 into the fixed-length utterance embedding 260. The fixed-length utterance embedding 260 includes a numerical vector representing a prosody of the reference audio signal 222. In some examples, the fixed-length utterance embedding 260 includes a numerical vector having a value equal to “256”. The encoder portion 302 may repeat this process for each reference audio signal 222. In some examples, the encoder portion 302 encodes a plurality of reference audio signals 222 each corresponding to a same spoken utterance/phrase but with varying prosodies, i.e., each reference audio signal 222 conveys the same utterance but is spoken differently. The fixed-length utterance embedding 260 may be stored in the data storage 180 (FIG. 1) along with a respective transcript 261 (e.g., textual representation) of the reference audio signal 222. From the transcript 261, linguistic features may be extracted and stored for use in conditioning the training of the hierarchical linguistic structure 200. The linguistic features may include, without limitation, individual sounds for each phoneme, whether each syllable is stressed or un-stressed, the type of each word (e.g., noun/adjective/verb) and/or the position of the word in the utterance, and whether the utterance is a question or phrase.

Referring to FIG. 2C, in some implementations, the hierarchical linguistic structure 200 omits the level associated with the sequence of phonemes 230 and allows the encoder portion 302 to simply encode a corresponding subset of reference frames 220 into each syllable 240 of the syllable level 240 during training. For instance, the first seven reference frames 220 may be encoded directly into syllable 240Aa without having to encode into corresponding phonemes 230Aa1, 230Aa2 (FIG. 2A) as an intermediary step. Similarly, during training, the hierarchical linguistic structure 200 may optionally omit the level associated with the sequence of words 250 and allow the encoder portion 302 to encode the sequence of syllables 240 directly into the fixed-length utterance embedding 260. In other examples, training may instead optionally include the level of associated with the sequence of phonemes 230 and allow the encoder portion 302 to simply encode a corresponding subset of reference frames 220 into each phoneme 230 of the level of phonemes 230 and then encode a corresponding subset of phonemes 230 directly into the fixed-length utterance embedding 260 without having to encode corresponding syllables 240 and/or words 250.

Referring to FIG. 2B, the decoder portion 310 of the variational autoencoder 300 is configured to produce a plurality of fixed-length syllable embeddings 245 by initially decoding a fixed-length utterance embedding 260 that represents a prosody for an utterance. During training, the utterance embedding 260 may include the utterance embedding 260 output from the encoder portion 302 of FIGS. 2A and 2C by encoding the plurality of fixed-length reference frames 220 sampled from the reference audio signal 222. Thus, the decoder portion 310 is configured to back-propagate the utterance embedding 260 during training to generate the plurality of fixed-length predicted frames 280 that closely match the plurality of fixed-length reference frames 220. For instance, fixed-length predicted frames 280 for both pitch (F0) and energy (C0) may be generated in parallel to represent a target prosody (e.g., predicted prosody) that substantially matches the reference prosody of the reference audio signal 222 input to the encoder portion 302 as training data. In some examples, a TTS system 150 (FIG. 1) uses the fixed-length predicted frames 280 to produce synthesized speech 152 with a selected prosody based on the fixed-length utterance embedding 260. For instance, a unit selection module or a WaveNet module of the TTS system 150 may use the frames 280 to produce the synthesized speech 152 having the intended prosody.

In the example shown, the decoder portion 310 decodes the utterance embedding 260 (e.g., numerical value of “256”) received from the encoder portion 302 (FIG. 2A or 2C) into hierarchical levels of words 250, 250 b, syllables 240, 240 b, phonemes 230, 230 b, and the fixed-length predicted frames 280. Specifically, the fixed-length utterance embedding 260 corresponds to a variational layer of hierarchical input data for the decoder portion 310 and each of the stacked hierarchical levels include Long Short-Term Memory (LSTM) processing cells variably clocked to a length of the hierarchical input data. For instance, the syllable level 240 clocks faster than the word level 250 and slower than the phoneme level 230. The rectangular blocks in each level correspond to LSTM processing cells for respective words, syllables, phonemes, or frames. Advantageously, the autoencoder 300 gives the LSTM processing cells of the word level 250 memory over the last 100 words, gives the LSTM cells of the syllable level 240 memory over the last 100 syllables, gives the LSTM cells of the phoneme level 230 memory over the last 100 phonemes, and gives the LSTM cells of the fixed-length pitch and/or energy frames 280 memory over the last 100 fixed-length frames 280. When the fixed-length frames 280 include a duration (e.g., frame rate) of five milliseconds each, the corresponding LSTM processing cells provide memory over the last 500 milliseconds (e.g., a half second).

In the example shown, the decoder portion 310 of the hierarchical linguistic structure 200 simply back-propagates the fixed-length utterance embedding 260 encoded by the encoder portion 302 into the sequence of three words 250A-250C, the sequence of five syllables 240Aa-240Cb, and the sequence of nine phonemes 230Aa1-230Cb2 to generate the sequence of predicted fixed-length frames 280. The decoder portion 310 is conditioned upon linguistic features of the input text. By contrast to the encoder portion 302 of FIGS. 2A and 2C where outputs from faster clocking layers are received as inputs by slower clocking layers, the decoder portion 310 includes outputs from slower clocking layers feeding faster clocking layers such that the output of a slower clocking layer is distributed to the input of the faster clocking layer at each clock cycle with a timing signal appended thereto.

Referring to FIGS. 3A-3C, in some implementations, the autoencoder 300 uses the hierarchical linguistic structure 200 to predict a prosodic representation for a given text utterance 320 during inference by jointly predicting durations of phonemes 230 and pitch and/or energy contours for each syllable 240 of the given text utterance 320. Since the text utterance 320 does not provide any context, semantic information, or pragmatic information to indicate an appropriate prosody for the text utterance, the autoencoder 300 selects an utterance embedding 260 as a latent variable to represent an intended prosody for the text utterance 320.

The utterance embedding 260 may be selected from the utterance embedding data storage 180 (FIG. 1). Each utterance embedding 260 in the storage 180 may be encoded by the encoder portion 302 (FIGS. 2A and 2C) from a corresponding variable-length reference audio signal 222 (FIGS. 2A and 2C) during training. Specifically, the encoder portion 302 compresses prosody of variable-length reference audio signals 222 into fixed-length utterance embeddings 260 during training and stores each utterance embedding 260 together with a transcript 261 of the corresponding reference audio signal 222 in the utterance embedding data storage 180 for use by the decoder portion 310 at inference. In the example shown, the autoencoder 300 may first locate utterance embeddings 260 having transcripts 261 that closely match the text utterance 320 and then select one of the utterance embeddings 260 to predict the prosodic representation 322 (FIG. 1) for the given text utterance 320. In some examples, the fixed-length utterance embedding 260 is selected by picking a specific point in a latent space of embeddings 260 that likely represents particular semantics and pragmatics for a target prosody. In other examples, the latent space is sampled to choose a random utterance embedding 260 for representing the intended prosody for the text utterance 320. In yet another example, the autoencoder 300 models the latent space as multidimensional unit Gaussian by choosing a mean of the utterance embeddings 260 having closely matching transcripts 261 for representing a most likely prosody for the linguistic features of the text utterance 320. If the prosody variation of the training data is reasonably neutral, the last example of choosing the mean of utterance embeddings 260 is a reasonable choice.

FIGS. 3A and 3C show the text utterance 320 having three words 250A, 250B, 250C represented in the word level 250 of the hierarchical linguistic structure 200. The first word 250A contains syllables 240Aa, 240Ab, the second word 250B contains one syllable 240Ba, and the third word 250C contains syllables 240Ca, 240Cb. Accordingly, the syllable level 240 of the hierarchical linguistic structure 200 includes a sequence of five syllables 240Aa-240Cb of the text utterance 320. At the syllable level 240 of LTSM processing cells, the autoencoder 300 is configured to produce/output a corresponding syllable embedding 245Aa, 245Ab, 245Ba, 245Ca, 245Cb for each syllable 240 from the following inputs: the fixed-length utterance embedding 260; utterance-level linguistic features 262 associated with the text utterance 320; word-level linguistic features 252 associated with the word 250 that contains the syllable 240; and syllable-level linguistic features 242 for the syllable 240. The utterance-level linguistic features 262 may include, without limitation, whether or not the text utterance 320 is a question, an answer to a question, a phrase, a sentence, etc. The word-level linguistic features 252 may include, without limitation, a word type (e.g., noun, pronoun, verb, adjective, adverb, etc.) and a position of the word in the text utterance 320. The syllable-level linguistic features 242 may include, without limitation, whether the syllable 240 is stressed or unstressed.

In the example shown, each syllable 240Aa, 240Ab, 240Ba, 240Ca, 240Cb in the syllable level 240 may be associated with a corresponding LTSM processing cell that outputs a corresponding syllable embedding 245Aa, 245Ab, 245Ba, 245Ca, 245Cb to the faster clocking phoneme level 230 for decoding the individual fixed-length predicted pitch (F0) frames 280, 280F0 (FIG. 3A) and for decoding the individual fixed-length predicted energy (C0) frames 280, 28000 (FIG. 3C) in parallel. FIG. 3A shows each syllable in the syllable level 240 including a plurality of fixed-length predicted pitch (F0) frames 280F0 that indicate a duration (timing and pauses) and a pitch contour for the syllable 240. Here, the duration and pitch contour correspond to a prosodic representation of the syllable 240. FIG. 3C shows each phoneme in the phoneme level 230 including a plurality of fixed-length predicted energy (C0) frames 28000 that indicate a duration and an energy contour for the phoneme.

The first syllable 240Aa (i.e., LTSM processing cell Aa) in the syllable level 240 receives the fixed-length utterance embedding 260, utterance-level linguistic features 262 associated with the text utterance 320, word-level linguistic features 252A associated with the first word 250A, and the syllable-level linguistic features 242Aa for the syllable 240Aa as inputs for producing the corresponding syllable embedding 245Aa. The second syllable 240Ab in the syllable level 240 receives the fixed-length utterance embedding 260, the utterance-level linguistic features 262 associated with the text utterance 320, the word-level linguistic features 252A associated with the first word 250A, and corresponding syllable-level linguistic features 242 (not shown) for the syllable 240Ab as inputs for producing the corresponding syllable embedding 245Ab. While the example only shows syllable-level linguistic features 242 associated with the first syllable 240Aa, the corresponding syllable-level linguistic features 242 associated with each other syllable 240Ab-240Cb in the syllable level 240 are only omitted from the views of FIGS. 3A and 3B for the sake of clarity.

For simplicity, the corresponding syllable-level linguistic features 242 input to the processing block for syllable 240Ab are not shown. The LTSM processing cell (e.g., rectangle Ab) associated with the second syllable 240Ab also receives the state of the preceding first syllable 240Aa. The remaining sequence of syllables 240Ba, 240Ca, 240Cb in the syllable level 240 each produce corresponding syllable embeddings 245Ba, 245Ca, 245Cb in a similar manner. For simplicity, the corresponding syllable-level linguistic features 242 input to the processing block for each of the syllables 240Ba, 240Ca, 240Cb are not shown. Moreover, each LTSM processing cell of the syllable level 240 receives the state of the immediately preceding LTSM processing cell of the syllable level 240.

Referring to FIG. 3A, the phoneme level 230 of the hierarchical linguistic structure 200 includes the sequence of nine phonemes 230Aa1-230Cb2 each associated with a corresponding predicted phoneme duration 234. Moreover, the autoencoder 300 encodes the phoneme-level linguistic features 232 associated with each phoneme 230Aa1-230Cb2 with the corresponding syllable embedding 245 for predicting the corresponding predicted phoneme duration 234 and for predicting the corresponding pitch (f0) contour for the syllable containing the phoneme. The phoneme-level linguistic features 232 may include, without limitation, an identity of sound for the corresponding phoneme 230. While the example only shows phoneme-level linguistic features 232 associated with the first phoneme 230Aa1, the phoneme-level linguistic features 232 associated with the other phonemes 230Aa2-230Cb2 in the phoneme level 230 are only omitted from the views of FIGS. 3A and 3C for the sake of clarity.

The first syllable 240Aa contains phonemes 230Aa1, 230Aa2 and includes a predicted syllable duration equal to the sum of the predicted phone durations 234 for the phonemes 230Aa1, 230Aa2. Here, the predicted syllable duration for the first syllable 240Aa determines the number of fixed-length predicted pitch (F0) frames 280F0 to decode for the first syllable 240Aa. In the example shown, the autoencoder 300 decodes a total of seven fixed-length predicted pitch (F0) frames 280F0 for the first syllable 240Aa based on the sum of the predicted phoneme durations 234 for the phonemes 230Aa1, 230Aa2. Accordingly, the faster clocking syllable layer 240 distributes the first syllable embedding 245Aa as an input to each phoneme 230Aa1, 230Aa2 included in the first syllable 240Aa. A timing signal may also be appended to the first syllable embedding 245Aa. The syllable level 240 also passes the state of the first syllable 240Aa to the second syllable 240Ab.

The second syllable 240Ab contains a single phoneme 230Ab1 and therefore includes a predicted syllable duration equal to the predicted phoneme duration 234 for the phoneme 230Ab1. Based on the predicted syllable duration for the second syllable 240Ab, the autoencoder 300 decodes a total of four fixed-length predicted pitch (F0) frames 280F0 for the second syllable 240Ab. Accordingly, the faster clocking syllable layer 240 distributes the second syllable embedding 245Ab as an input to the phoneme 230Ab1. A timing signal may also be appended to the second syllable embedding 245Aa. The syllable level 240 also passes the state of the second syllable 240Ab to the third syllable 240Ba.

The third syllable 240Ba contains phonemes 230Ba1, 230Ba2, 230Ba3 and includes a predicted syllable duration equal to the sum of the predicted phoneme durations 234 for the phonemes 230Ba1, 230Ba2, 230Ba3. In the example shown, the autoencoder 300 decodes a total of eleven fixed-length predicted pitch (F0) frames 280F0 for the third syllable 240Ba based on the sum of the predicted phoneme durations 234 for the phonemes 230Ba1, 230Ba2, 230Ba3. Accordingly, the faster clocking syllable layer 240 distributes the third syllable embedding 245Ba as an input to each phoneme 230Ba1, 230Ba2, 230Ba3 included in the third syllable 240Ba. A timing signal may also be appended to the third syllable embedding 245Ba. The syllable level 240 also passes the state of the third syllable 240Ba to the fourth syllable 240Ca.

The fourth syllable 240Ca contains a single phoneme 230Ca1 and therefore includes a predicted syllable duration equal to the predicted phoneme duration 234 for the phoneme 230Ca1. Based on the predicted syllable duration for the fourth syllable 240Ca, the autoencoder 300 decodes a total of three fixed-length predicted pitch (F0) frames 280F0 for the fourth syllable 240Ca. Accordingly, the faster clocking syllable layer 240 distributes the fourth syllable embedding 245Ca as an input to the phoneme 230Ca1. A timing signal may also be appended to the fourth syllable embedding 245Ca. The syllable level 240 also passes the state of the fourth syllable 240Ba to the fifth syllable 240Cb.

Lastly, the fifth syllable 240Cb contains phonemes 230Cb1, 230Cb2 and includes a predicted syllable duration equal to the sum of the predicted phoneme durations 234 for the phonemes 230Cb1, 230Cb2. In the example shown, the autoencoder 300 decodes a total of six fixed-length predicted pitch (F0) frames 280F0 for the fifth syllable 240Cb based on the sum of the predicted phoneme durations 234 for the phonemes 230Cb1, 230Cb2. Accordingly, the faster clocking syllable layer 240 distributes the fifth syllable embedding 245Cb as an input to each phoneme 230Cb1, 230Cb2 included in the fifth syllable 240Cb. A timing signal may also be appended to the fifth syllable embedding 245Cb.

FIG. 3B provides a detailed view within dashed box 350 of FIG. 3A to show the decoding of the first syllable embedding 245Aa into individual fixed-length predicted pitch (F0) frames 280F0 for the first syllable 240Aa. As set forth above with reference to FIG. 3A, the autoencoder 300 determines the number of fixed-length predicted pitch (F0) frames 280 to decode based on the predicted syllable duration for the first syllable 240Aa. The first syllable 240Aa generates the corresponding first syllable embedding 245Aa for distribution as an input to each of the first and second phonemes 230Aa1, 230Aa2 of the faster clocking syllable level 240.

At the phoneme level 230 of the hierarchical linguistic structure 200, the autoencoder 300 predicts the phoneme duration 234 for the first phoneme 230Aa1 by encoding the phoneme-level linguistic features 232 associated with the first phoneme 230Aa1 with the first syllable embedding 245Aa. Likewise, the autoencoder 300 predicts the phoneme duration 234 for the second phoneme 230Aa2 by encoding the phoneme-level linguistic features (not shown) associated with the second phoneme 230Aa2 with the first syllable embedding 245Aa. The second phoneme 230Aa2 also receives the previous state from the first phoneme 230Aa1. The predicted syllable duration for the first syllable 230Aa is equal to the sum of the predicted phone durations 234 for the first and second phonemes 230Aa1, 230Aa2. The encodings of the first syllable embedding 245Aa with the corresponding phoneme-level linguistic features 232 associated with each of the phonemes 230Aa1, 230Aa2 is further combined with the first syllable embedding 245Aa at the output of the phoneme level 230 to predict the pitch (F0) for the first syllable 240Aa and generate the fixed-length predicted pitch (F0) frames 280F0 for the first syllable 240Aa. In the example shown, the autoencoder 300 determines the total number (e.g., seven) of fixed-length predicted pitch (F0) frames 280F0 to decode/generate based on the predicted syllable duration for the first syllable 240Aa. Thus, the fixed-length predicted pitch (F0) frames 280 decoded from the first syllable embedding 245Aa collectively indicate a corresponding duration and pitch contour for the first syllable 240Aa of the text utterance 320.

Referring back to FIG. 3A, the autoencoder 300 similarly decodes each of the remaining syllable embeddings 245Ab, 245Ba, 245Ca, 245Cb output from the syllable level 240 into individual fixed-length predicted pitch (F0) frames 280 for each corresponding syllable 240Ab, 240Ba, 240Ca, 240Cb. For instance, the second syllable embedding 245Ab is further combined at the output of the phoneme level 230 with the encoding of the second syllable embedding 245Ab and the corresponding phoneme-level linguistic features 232 associated with the phoneme 230Ab1, while the third syllable embedding 245Ba is further combined at the output of the phoneme level 230 with the encodings of the third syllable embedding 245Ba and the corresponding phoneme-level linguistic features 232 associated with each of the phonemes 230Ba1, 230Ba2, 230Ba3. Moreover, the fourth syllable embedding 245Ca is further combined at the output of the phoneme level 230 with the encodings of the fourth syllable embedding 245Ca and the corresponding phoneme-level linguistic features 232 associated with the phoneme 230Ca1, while the fifth syllable embedding 245Cb is further combined at the output of the phoneme level 230 with the encodings of the fifth syllable embedding 245Cb and the corresponding phoneme-level linguistic features 232 associated with each of the phonemes 230Cb1, 230Cb2. While the fixed-length predicted pitch (F0) frames 280F0 generated by the autoencoder 300 include frame-level LSTM, other configurations may replace the frame-level LSTM of pitch (F0) frames 280F0 with a feed-forward layer so that the pitch (F0) of every frame in a corresponding syllable is predicted in one pass.

Referring now to FIG. 3C, the autoencoder 300 is further configured to encode the phoneme-level linguistic features 232 associated with each phoneme 230Aa1-230Cb2 with the corresponding syllable embedding 245 for predicting the corresponding energy (C0) contour for each phoneme 230. The phoneme-level linguistic features 232 associated with phonemes 230Aa2-230Cb2 in the phoneme level 230 are only omitted from the view of FIG. 3C for the sake of clarity. The autoencoder 300 determines the number of fixed-length predicted energy (C0) frames 280, 28000 to decode for each phoneme 230 based on the corresponding predicted phoneme duration 234. For instance, the autoencoder 300 decodes/generates four (4) predicted energy (C0) frames 28000 for the first phoneme 230Aa1, three (3) predicted energy (C0) frames 28000 for the second phoneme 230Aa2, four (4) predicted energy (C0) frames 28000 for the third phoneme 230Ab1, two (2) predicted energy (C0) frames 28000 for the fourth phoneme 230Ba1, five (5) predicted energy (C0) frames 28000 for the fifth phoneme 230Ba2, four (4) predicted energy (C0) frames 28000 for the sixth phoneme 230Ba3, three (3) predicted energy (C0) frames 28000 for the seventh phoneme 230Ca1, four (4) predicted energy (C0) frames 28000 for the eighth phoneme 230Cb1, and two (2) predicted energy (C0) frames 28000 for the ninth phoneme 230Cb2. Accordingly, as with the predicted phoneme duration 234, the predicted energy contour for each phoneme in the phoneme level 230 is based on an encoding between the syllable embedding 245 input from the corresponding syllable in the slower clocking syllable level 240 that contains the phoneme and the linguistic features 232 associated with the phoneme.

FIG. 4 is a flow chart of an example arrangement of operations for a method 400 of predicting a prosodic representation 322 for a text utterance 320. The method 400 may be described with reference to FIGS. 1-3C. The memory hardware 124 residing on the computer system 120 of FIG. 1 may store instructions that when executed by the data processing hardware 122 cause the data processing hardware 122 to execute the operations for the method 400. At operation 402, the method 400 includes receiving the text utterance 320. The text utterance 320 has at least one word, each word having at least one syllable, each syllable having at least one phoneme. At operation 404, the method 400 includes selecting an utterance embedding 260 for the text utterance 320. The utterance embedding 260 represents an intended prosody. As used herein, the selected utterance embedding 260 is used to predict the prosodic representation 322 of the text utterance 320 for use by a TTS system 150 to produce synthesized speech 152 from the text utterance 320 and having the intended prosody. The utterance embedding 260 may be represented by a fixed-length numerical vector. The numerical vector may include a value equal to “256”. To select the utterance embedding 260 for the text utterance 230, the data processing hardware 122 may first query the data storage 180 to locate utterance embeddings 260 having transcripts 261 that closely match the text utterance 320 and then select the utterance embeddings 260 to predict the prosodic representation 322 for the given text utterance 320. In some examples, the fixed-length utterance embedding 260 is selected by picking a specific point in a latent space of embeddings 260 that likely represents particular semantics and pragmatics for a target prosody. In other examples, the latent space is sampled to choose a random utterance embedding 260 for representing the intended prosody for the text utterance 320. In yet another example, the data processing hardware 122 models the latent space as multidimensional unit Gaussian by choosing a mean of the utterance embeddings 260 having closely matching transcripts 261 for representing a most likely prosody for the linguistic features of the text utterance 320. If the prosody variation of the training data is reasonably neutral, the last example of choosing the mean of utterance embeddings 260 is a reasonable choice

At operation 406, for each syllable 240, using the selected utterance embedding 260, the method 400 includes predicting a duration of the syllable by encoding linguistic features 232 of each phoneme 230 of the syllable with a corresponding prosodic syllable embedding 245 for the syllable. For instance, for each phoneme 230 associated with the syllable 240, the method 400 may predict a duration 234 of the corresponding phoneme 230 by encoding the linguistic features 232 of the corresponding phoneme 230 with the corresponding prosodic syllable embedding 245 for the syllable 240. Thereafter, the method 400 may predict the duration of the syllable 240 by summing the predicted durations 234 for each phoneme 230 associated with the syllable 240.

At operation 408, for each syllable 240, using the selected utterance embedding 260, the method 400 includes predicting a pitch contour of the syllable based on the predicted duration for the syllable. At operation 410, for each syllable, using the selected utterance embedding 260, the method 400 also includes generating a plurality of fixed-length predicted pitch frames 280, 280F0 based on the predicted duration for the syllable 240. Each fixed-length predicted pitch frame 280F0 represents part of the predicted contour of the syllable 240.

Additional operations for the method 400 may further include, for each syllable 240, using the selected utterance embedding 260, predicting an energy contour of each phoneme 230 in the syllable 240 based on a predicted duration 234 for the corresponding phoneme 230. For each phoneme 230 associated with the syllable, the method 400 may generate a plurality of fixed-length predicted energy frames 280, 28000 based on the predicted duration 234 for the corresponding phoneme 230. Here, each fixed-length energy frame 28000 represents the predicted energy contour of the corresponding phoneme 230.

Referring to FIGS. 5A and 5B, in some implementations, the hierarchical linguistic structure (e.g., deep neural network of FIG. 1) 200 for the clockwork hierarchical variational autoencoder 300 is adapted to provide a controllable model for predicting mel spectral information for an input text utterance 320 (FIG. 1), while at the same time effectively controlling the prosody implicitly represented in the mel spectral information. Specifically, the autoencoder 300 may predict a mel-frequency spectrogram 502 for the input text utterance (simply referred to as “input text” 320) and provide the mel-frequency spectrogram 502 as input to a vocoder network 155 of the TTS system 150 for conversion into a time-domain audio waveform. A time-domain audio waveform includes an audio waveform that defines an amplitude of an audio signal over time. As will become apparent, the TTS system 150 can generate synthesized speech 152 from input text 320 using the autoencoder 300 trained on sample input text and corresponding mel-frequency spectrograms 502 of human speech alone. That is, the autoencoder model 300 does not receive complex linguistic and acoustic features that require significant domain expertise to produce, but rather is able to convert input text 320 to mel-frequency spectrograms 502 using an end-to-end deep neural network (e.g., hierarchical linguistic structure) 200. The vocoder network 155, i.e., neural vocoder, is separately trained and conditioned on mel-frequency spectrograms for conversion into time-domain audio waveforms.

A mel-frequency spectrogram includes a frequency-domain representation of sound. Mel-frequency spectrograms emphasize lower frequencies, which are critical to speech intelligibility, while de-emphasizing high frequency, which are dominated by fricatives and other noise bursts and generally do not need to be modeled with high fidelity. The vocoder network 155 can be any network that is configured to receive mel-frequency spectrograms and generate audio output samples based on the mel-frequency spectrograms. For example, the vocoder network 155 can be, or can be based on the parallel feed-forward neural network described in van den Oord, Parallel WaveNet: Fast High-Fidelity Speech Synthesis, available at https://arxiv.org/pdf/1711.10433.pdf, and incorporated herein by reference. Alternatively, the vocoder network 155 can be an autoregressive neural network.

As described above with reference to FIGS. 2A and 2B, the autoencoder 300 includes an encoder portion 302 (FIG. 5A) and a decoder portion 310 (FIG. 5B). The encoder portion 302 is configured to encode a plurality of fixed-length reference mel-frequency spectrogram frames 520 sampled/extracted from a reference audio signal 222 into a fixed-length mel spectral embedding 560. The decoder portion 310 is configured to learn how to decode the fixed-length mel spectral embedding 560 into a plurality of fixed-length predicted mel-frequency spectrogram frames 580. The autoencoder may be trained so that the number of predicted mel-frequency spectrogram frames 580 output from the decoder portion 310 is equal to the number of reference mel-frequency spectrogram frames 520 input to the encoder portion 302. Moreover, the autoencoder is trained so that data associated with the reference and predicted mel-frequency spectrogram frames 520, 580 substantially match one another. The predicted mel-frequency spectrogram frames 580 may implicitly provide a prosodic representation of the reference audio signal 222.

Referring to FIG. 5A, the encoder portion 302 receives the sequence of fixed-length reference mel-frequency spectrogram (MFS) frames 520 extracted from the input reference audio sample 222. The input reference audio signal 222 may include a spoken utterance of human speech recorded by a microphone. The encoder portion may receive multiple reference audio signals 222 for a same spoken utterance, but spoken in multiple different ways and/or spoken by multiple different people/speakers. As with the reference frames 220 of FIG. 2A representing a contour of pitch (F0) or a contour of energy (C0), the reference MFS frames 520 may each include a duration of five (5) milliseconds (ms). The length of each fixed-length reference MFS frame 520 may include a duration less than 5 ms or greater than 5 ms. For example, the duration may be equal to 12.5 ms. The encoder portion 302 includes the hierarchical levels (e.g., LTSM levels) of reference MFS frames 520, phonemes 230,230 a, syllables 240, 240 a, and words 250, 250 a for the reference audio signal 222 that clock relative to one another. As discussed above with reference to FIG. 2A, the slower clocking layers receive, as input, an output from faster clocking layers so that the output after the final clock (i.e., state) of a faster layer is taken as the input to the corresponding slower layer to essentially provide a sequence-to-sequence encoder. In some scenarios, the width of each level is equal to 256 cells. In other scenarios, increasing the level width from 256 cells to 1,024 cells improves performance for driving the vocoder network 155. In such scenarios, Tensor Processing Units (TPU) may be employed to achieve faster training times for increased model size due to increasing the LTSM level width.

In some examples, the encoder portion 302 first encodes the sequence of reference MFS frames 520 into the sequence of phonemes 230. Each phoneme 230 receives, as input, a corresponding encoding of a subset of reference MFS frames 520 and includes a duration equal to the number of reference MFS frames 520 encoded into the phoneme 230 and corresponding mel-frequency spectrograms. After encoding the fixed-length reference MFS frames 520 into the corresponding phonemes 230, the encoder portion 302 is configured to: next encode the sequence of phonemes 230 into the sequence of syllables 240; then encode the sequence of syllables 240 into the sequence of words 250; and last encode the sequence of words 250 into the fixed-length mel spectral embedding 560. The encoding of each hierarchical level of slower clocking levels receiving as input, the output of the faster clocking layers occurs in the same manner as discussed above with reference to the encoder portion 302 of FIG. 2A. The fixed-length mel spectral embedding 560 includes a numerical vector that implicitly represents a prosody of the reference audio signal 222. The encoder portion 302 may encode a plurality of reference audio signals 222 that may correspond to a same spoken utterance/phrase but spoken in multiple different ways and/or spoken by multiple different people/speakers. The mel spectral embedding 560 may be stored in the data storage 180 (FIG. 1) along with a respective transcript 261 (e.g., textual representation) of the reference audio signal 222. From the transcript 261, linguistic features may be extracted and stored for use in conditioning the training of the hierarchical linguistic structure 200. The linguistic features may include, without limitation, individual sounds for each phoneme, whether each syllable is stressed or un-stressed, the type of each word (e.g., noun/adjective/verb) and/or the position of the word in the utterance, and whether the utterance is a question or phrase.

As discussed above with reference to FIG. 2C, the hierarchical linguistic structure 200 may omit the level associated with the sequence of phonemes 230 and allow the encoder portion 302 to simply encode a corresponding subset of reference MFS frames 220 into each syllable 240 of the syllable level 240 during training. Similarly, during training, the sequence of words 250 may be omitted from the structure 200 to allow the encoder portion 302 to encode the sequence of syllables 240 directly into the fixed-length mel spectral embedding 560. In other examples, the encoder portion 302 may encode a corresponding subset of reference MFS frames 220 directly into the fixed-length mel spectral embedding 560 without having to encode corresponding syllables 240 and/or words 250.

Referring to FIG. 5B, the decoder portion of the variational autoencoder 300 is configured to produce a plurality of fixed-length syllable embedding 245 by initially decoding a fixed-length mel spectral embedding 560 that includes a numerical vector implicitly representing the prosodic information for the input text 322. During training, the mel spectral embedding 560 may include the mel spectral embedding 560 output from the encoder portion 302 of FIGS. 5A and 5B by encoding the plurality of fixed-length reference MFS frames 520 sampled from the reference audio signal 222. Thus, the decoder portion 310 is configured to back-propagate the mel spectral embedding 560 during training to generate the plurality of fixed-length predicted MFS frames 580 that closely match the plurality of fixed-length reference MFS frames 520. The vocoder network 155 (FIG. 1) of the TTS system 150 (FIG. 1) may convert the fixed-length predicted MFS frames 580 into a time-domain audio waveform to produce synthesized speech 152.

As discussed above with reference to the decoder portion 310 of FIG. 2B decoding the utterance embedding 260, the decoder portion 310 of FIG. 5B similarly decodes the mel-spectral embedding 560 received from the encoder portion 302 (FIG. 5A) into hierarchical levels of words 250, 250 b, syllables 240, 240 b, phonemes 230, 230 b, and the fixed-length predicted frames 280. Specifically, the fixed-length mel-spectral embedding 560 corresponds to a variational layer of hierarchical input data for the decoder portion 310 and each of the stacked hierarchical levels include LSTM processing cells variably clocked to a length of the hierarchical input data. For instance, the syllable level 240 clocks faster than the word level 250 and slower than the phoneme level 230. The rectangular blocks in each level correspond to LSTM processing cells for respective words, syllables, phonemes, or frames.

During training, the decoder portion 302 simply back-propagates the fixed-length mel-spectral embedding 560 encoded by the encoder portion 302 into the sequence of three words 250A-250C, the sequence of five syllables 240Aa-240Cb, and the sequence of nine phonemes 230Aa1-230Cb2 to generate the sequence of fixed-length predicted MFS frames 580. By contrast to the encoder portion 302 of FIG. 5A where outputs from faster clocking layers are received as inputs by slower clocking layers, the decoder portion 310 includes outputs from slower clocking layers feeding faster clocking layers such that the output of a slower clocking layer is distributed to the input of the faster clocking layer at each clock cycle with a timing signal appended thereto. The decoder portion 310 is conditioned upon linguistic features of the input text 320, where the input text 320 may correspond to the transcript 261 of the reference audio signal 222 from which the corresponding encoded fixed-length reference MFS frames 520 were sampled. The linguistic features may include: utterance-level linguistic features 262 associated with the input text 322 (transcript 261); word-level linguistic features 252 associated with each word 250 in the input text 322; and syllable-level linguistic features 242 associated with each syllable 240 in the input text 322. The utterance-level linguistic features 262 may include, without limitation, whether or not the input text 320 is a question, an answer to a question, a phrase, a sentence, etc. The word-level linguistic features 252 may include, without limitation, a word type (e.g., noun, pronoun, verb, adjective, adverb, etc.) and a position of the word in the input text 320. The syllable-level linguistic features 242 may include, without limitation, whether the syllable 240 is stressed or unstressed.

During inference, the autoencoder 300 uses the hierarchical linguistic structure 200 to predict the fixed-length MFS frames 280 for a given input text (e.g., text utterance) 320. Since the input text 320 does not provide any context, semantic information, or pragmatic information to indicate an appropriate prosody for the input text 320, the autoencoder 300 may use the mel-spectral embedding 560 as a latent variable to represent an intended prosody for the text utterance. The mel-spectral embedding 560 may be selected from the data store 180 and correspond to an encoding by the encoder portion 302 from a corresponding variable-length reference audio signal 222 (FIG. 5A) during training. The autoencoder 300 may select the mel-spectral embedding 560 having a transcript 261 that closely matches the input text 320. At the syllable level 240 of LTSM processing cells, the autoencoder 300 is configured to produce/output a corresponding syllable embedding 245Aa, 245Ab, 245Ba, 245Ca, 245Cb for each syllable 240 from the following inputs: the fixed-length mel-spectral embedding 560; utterance-level linguistic features 262 associated with the input text 320; word-level linguistic features 252 associated with the word 250 that contains the syllable 240; and syllable-level linguistic features 242 for the syllable 240. While the example only shows syllable-level linguistic features 242 associated with the first syllable 240Aa, the corresponding syllable-level linguistic features 242 associated with each other syllable 240Ab-240Cb in the syllable level 240 are only omitted from the view of FIG. 5B for the sake of clarity. Moreover, each LTSM processing cell of the syllable level 240 receives the state of the immediately preceding LTSM processing cell of the syllable level 240.

The autoencoder is further configured to encode the phoneme-level linguistic features 232 associated with each phoneme 230Aa1-230Cb2 with the corresponding syllable embedding 245, whereby phoneme-level linguistic features 232 associated with phonemes 230Aa2-230Cb2 in the phoneme level 230 are only omitted from the view of FIG. 5B for the sake of clarity. The autoencoder 300 determines the number of fixed-length predicted MFS frames 580 to decode for each phoneme 230 based on the corresponding predicted phoneme duration 234 (e.g., as shown in FIG. 3C).

Accordingly, as with the predicted phoneme duration 234, the predicted mel-frequency spectrogram associated with each phoneme in the phoneme level 230 is based on an encoding between the syllable embedding 245 input from the corresponding syllable in the slower clocking syllable level 240 that contains the phoneme and the linguistic features 232 associated with the phoneme.

In some scenarios, the autoencoder 300 predicts the mel-spectral representation (e.g., mel-frequency spectrogram 502) for input text (e.g., text utterance) 320 without predicting any of the aforementioned prosodic information, such as prosodic features of duration, pitch (F0), and energy (C0) (e.g., DFC features). In other scenarios, the autoencoder 300 predicts the mel-spectral representation for input text 320 in parallel with predicting any combination of the prosodic features. In these scenarios, the autoencoder 300 may be jointly trained to predict the sequence of fixed-length MFC frames 580 representing the mel-frequency spectrogram 502 for the input text 320 as well to predict, for each syllable of the input text, a duration of all phonemes in the syllable and pitch (F0) and/or energy (C0) contours for the syllable without relying on any unique mappings from the input text or other linguistic specification. For instance, the autoencoder 300 may explicitly predict the pitch contour (F0) for the syllable (as discussed in FIGS. 3A and 3B) in parallel with predicting the mel-frequency spectrogram 502.

In some implementations, the autoencoder 300 omits using the encoder portion 302 of FIG. 5A to encode the mel-spectral embedding 560, but uses the encoder portion 302 to encode the prosodic information represented by the utterance embedding 260 (FIG. 2A). Here, using the utterance embedding 260, the decoder portion 302 may be jointly trained to predict the duration 234 for each of the phonemes to predict the fixed-length energy frames (C0) 28000 (FIG. 3C) as well as the fixed-length predicted MFC frames 580 (FIG. 5B).

In some examples, a framerate and the frame-shift of the fixed-length predicted MFS frames 580 output by the decoder portion 310 of the autoencoder 300 is adjustable to drive the vocoder network 155. For instance, such adjusting may be necessitated when the vocoder network 155 includes to a pre-existing and previously-trained vocoder network having a slower (or faster) and non-integer multiple of the frame-shift associated with the autoencoder 300. In additional examples, a post-filter may be applied to sharpen the mel-frequency spectrogram 502 predicted by the autoencoder 300 when the predicted mel-frequency spectrogram 502 appears over-smoothed. Additionally, a speaker d-vector representation may be used to drive the vocoder network 155. In some examples, weakening the speaker d-vector representation prevent capturing intonation or other aspects captured by the utterance embedding 260 and/or the mel-spectral embedding 560. That is, the speaker d-vector representation may be weakened by applying bottleneck/drop-out or adding noise via a variational layer plus KL loss. Here, the amount of information (e.g., prosodic information) that the speaker d-vector representation contributes may be controlled by tuning a KL loss weight or by setting a target KL loss value.

FIG. 6 is a flow chart of an example arrangement of operations for a method 600 of predicting a mel-frequency spectrogram 502 for a text utterance 320. The method 600 may be described with reference to FIGS. 1-5B. The memory hardware 124 residing on the computer system 120 of FIG. 1 may store instructions that when executed by the data processing hardware 122 cause the data processing hardware 122 to execute the operations for the method 600. At operation 602, the method 600 includes receiving the text utterance 320. The text utterance 320 has at least one word, each word having at least one syllable, each syllable having at least one phoneme. At operation 604, the method 400 includes selecting a mel spectral embedding 560 for the text utterance 320. The embedding 560 may implicitly represent an intended prosody. As used herein, the embedding 260 is used to predict the mel-frequency spectrogram 502 of the text utterance 320 for use by a TTS system 150 to produce synthesized speech 152 from the text utterance 320. Specifically, the TTS system 150 may include a vocoder network (e.g., neural vocoder) 155 that receives the mel-frequency spectrogram 502 as input, and converts the mel-frequency spectrogram 502 into a time-domain audio waveform that may be output from an audible output device as the synthesized speech 152. The mel spectral embedding 560 may be represented by a fixed-length numerical vector. The numerical vector may include a value equal to “1,024” or “256”.

For each phoneme, using the selected mel spectral embedding 560, the method 600 also includes, at operation 606, predicting a duration 234 of the corresponding phoneme 230 by encoding linguistic features 232 of the corresponding phoneme with a corresponding syllable embedding 245 for the syllable 240 that includes the corresponding phoneme 230. Additionally, for each phoneme, using the selected mel spectral embedding 560, the method 600 also includes, at operation 608, generating a plurality of fixed-length predicted mel-frequency spectrogram frames 580 based on the predicted duration 234 for the corresponding phoneme 230. In some examples, the method 400 uses the vocoder network 155 to convert the plurality of generated fixed-length predicted mel-frequency spectrogram frames 580 into a time-domain audio waveform. Here, vocoder network may be conditioned on mel-frequency spectrograms for conversion into time-domain audio waveforms. Moreover, the vocoder network 155 and a deep neural network 200 representing a hierarchical linguistic structure of the text utterance 322 may be trained separately.

A software application (i.e., a software resource) may refer to computer software that causes a computing device to perform a task. In some examples, a software application may be referred to as an “application,” an “app,” or a “program.” Example applications include, but are not limited to, system diagnostic applications, system management applications, system maintenance applications, word processing applications, spreadsheet applications, messaging applications, media streaming applications, social networking applications, and gaming applications.

The non-transitory memory may be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by a computing device. The non-transitory memory may be volatile and/or non-volatile addressable semiconductor memory. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM)/programmable read-only memory (PROM)/erasable programmable read-only memory (EPROM)/electronically erasable programmable read-only memory (EEPROM) (e.g., typically used for firmware, such as boot programs). Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM) as well as disks or tapes.

FIG. 7 is schematic view of an example computing device 700 (e.g., computing system 120 of FIG. 1) that may be used to implement the systems and methods described in this document. The computing device 700 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document.

The computing device 700 includes a processor 710, memory 720, a storage device 730, a high-speed interface/controller 740 connecting to the memory 720 and high-speed expansion ports 750, and a low speed interface/controller 760 connecting to a low speed bus 770 and a storage device 730. Each of the components 710, 720, 730, 740, 750, and 760, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 710 (e.g., data processing hardware 122 of FIG. 1)) can process instructions for execution within the computing device 700, including instructions stored in the memory 720 (e.g., memory hardware 124 of FIG. 1) or on the storage device 730 (e.g., memory hardware 124 of FIG. 1) to display graphical information for a graphical user interface (GUI) on an external input/output device, such as display 780 coupled to high speed interface 740. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 700 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

The memory 720 stores information non-transitorily within the computing device 700. The memory 720 may be a computer-readable medium, a volatile memory unit(s), or non-volatile memory unit(s). The non-transitory memory 720 may be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by the computing device 700. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM)/programmable read-only memory (PROM)/erasable programmable read-only memory (EPROM)/electronically erasable programmable read-only memory (EEPROM) (e.g., typically used for firmware, such as boot programs).

Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM) as well as disks or tapes.

The storage device 730 is capable of providing mass storage for the computing device 700. In some implementations, the storage device 730 is a computer-readable medium. In various different implementations, the storage device 730 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. In additional implementations, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 720, the storage device 730, or memory on processor 710.

The high speed controller 740 manages bandwidth-intensive operations for the computing device 700, while the low speed controller 760 manages lower bandwidth-intensive operations. Such allocation of duties is exemplary only. In some implementations, the high-speed controller 740 is coupled to the memory 720, the display 780 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 750, which may accept various expansion cards (not shown). In some implementations, the low-speed controller 760 is coupled to the storage device 730 and a low-speed expansion port 790. The low-speed expansion port 790, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet), may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.

The computing device 700 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 700 a or multiple times in a group of such servers 700 a, as a laptop computer 700 b, or as part of a rack server system 700 c.

Various implementations of the systems and techniques described herein can be realized in digital electronic and/or optical circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, non-transitory computer readable medium, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.

The processes and logic flows described in this specification can be performed by one or more programmable processors, also referred to as data processing hardware, executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, one or more aspects of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.

A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. 

What is claimed is:
 1. A computer-implemented method that when executed on data processing hardware causes the data processing hardware to perform operations comprising: receiving a text utterance having at least one word, each word having at least one syllable, each syllable having at least one phoneme; selecting a mel spectral embedding for the text utterance; and for each phoneme, using the selected mel spectral embedding: predicting a duration of the corresponding phoneme based on corresponding linguistic features associated with the word that includes the corresponding phoneme and corresponding linguistic features associated with the syllable that includes the corresponding phoneme; and generating a plurality of fixed-length predicted mel-frequency spectrogram frames based on the predicted duration for the corresponding phoneme, each fixed-length predicted mel-frequency spectrogram frame representing mel-spectral information of the corresponding phoneme.
 2. The computer-implemented method of claim 1, wherein, for each phoneme, using the selected mel spectral embedding, predicting the duration of the corresponding phoneme is further based on linguistic features of the corresponding phoneme.
 3. The computer-implemented method of claim 1, wherein the corresponding linguistic features associated with the word that includes the corresponding phoneme comprise a word type of the corresponding word and/or a position of the corresponding word in the text utterance.
 4. The computer-implemented method of claim 1, wherein the corresponding linguistic features associated with the syllable that includes the corresponding phoneme include whether the corresponding syllable is stressed or unstressed.
 5. The computer-implemented method of claim 1, wherein, for each phoneme, using the selected mel spectral embedding, predicting the duration of the corresponding phoneme is further based on linguistic features associated with the text utterance.
 6. The computer-implemented method of claim 1, wherein the operations further comprise converting, using a vocoder network, the plurality of fixed-length predicted mel-frequency spectrogram frames generated for each phoneme of the text utterance into a time-domain audio waveform.
 7. The computer-implemented method of claim 6, wherein the vocoder network is conditioned on mel-frequency spectrograms for conversion into time-domain audio waveforms.
 8. The computer-implemented method of claim 1, wherein the mel spectral embedding comprises a fixed-length numerical vector.
 9. The computer-implemented method of claim 1, wherein a network representing a hierarchical linguistic structure of the text utterance comprises: a first level comprising a corresponding long short-term memory (LSTM) processing cell representing each syllable of the text utterance; a second level comprising a corresponding LSTM processing cell representing each phoneme of the text utterance, the LSTM processing cells of the second level clocking relative to and faster than the LSTM processing cells of the first level; and a third level comprising a corresponding LSTM processing cell representing each fixed-length predicted mel-frequency spectrogram frame, the LSTM processing cells of the third level clocking relative to and faster than the LSTM processing cells of the second level.
 10. The computer-implemented method of claim 1, wherein the operations further comprise: receiving training data including a plurality of reference audio signals, each reference audio signal comprising a spoken utterance of human speech and having a corresponding prosody; and training a deep neural network for a mel-frequency spectrogram model by: encoding each reference audio signal into a corresponding fixed-length utterance embedding representing the corresponding prosody of the reference audio signal; and decoding each corresponding fixed-length utterance embedding into a plurality of corresponding fixed-length mel-frequency spectrogram frames representing the corresponding prosody of the reference audio signal.
 11. A system comprising: data processing hardware; and memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising: receiving a text utterance having at least one word, each word having at least one syllable, each syllable having at least one phoneme; selecting a mel spectral embedding for the text utterance; for each phoneme, using the selected mel spectral embedding: predicting a duration of the corresponding phoneme based on corresponding linguistic features associated with the word that includes the corresponding phoneme and corresponding linguistic features associated with the syllable that includes the corresponding phoneme; and generating a plurality of fixed-length predicted mel-frequency spectrogram frames based on the predicted duration for the corresponding phoneme, each fixed-length predicted mel-frequency spectrogram frame representing mel-spectral information of the corresponding phoneme.
 12. The system of claim 11, wherein, for each phoneme, using the selected mel spectral embedding, predicting the duration of the corresponding phoneme is further based on linguistic features of the corresponding phoneme.
 13. The system of claim 11, wherein the corresponding linguistic features associated with the word that includes the corresponding phoneme comprise a word type of the corresponding word and/or a position of the corresponding word in the text utterance.
 14. The system of claim 11, wherein the corresponding linguistic features associated with the syllable that includes the corresponding phoneme include whether the corresponding syllable is stressed or unstressed.
 15. The system of claim 11, wherein, for each phoneme, using the selected mel spectral embedding, predicting the duration of the corresponding phoneme is further based on linguistic features associated with the text utterance.
 16. The system of claim 11, wherein the operations further comprise converting, using a vocoder network, the plurality of fixed-length predicted mel-frequency spectrogram frames generated for each phoneme of the text utterance into a time-domain audio waveform.
 17. The system of claim 16, wherein the vocoder network is conditioned on mel-frequency spectrograms for conversion into time-domain audio waveforms.
 18. The system of claim 11, wherein the mel spectral embedding comprises a fixed-length numerical vector.
 19. The system of claim 11, wherein a network representing a hierarchical linguistic structure of the text utterance comprises: a first level comprising a corresponding long short-term memory (LSTM) processing cell representing each syllable of the text utterance; a second level comprising a corresponding LSTM processing cell representing each phoneme of the text utterance, the LSTM processing cells of the second level clocking relative to and faster than the LSTM processing cells of the first level; and a third level comprising a corresponding LSTM processing cell representing each fixed-length predicted mel-frequency spectrogram frame, the LSTM processing cells of the third level clocking relative to and faster than the LSTM processing cells of the second level.
 20. The system of claim 11, wherein the operations further comprise: receiving training data including a plurality of reference audio signals, each reference audio signal comprising a spoken utterance of human speech and having a corresponding prosody; and training a deep neural network for a mel-frequency spectrogram model by: encoding each reference audio signal into a corresponding fixed-length utterance embedding representing the corresponding prosody of the reference audio signal; and decoding each corresponding fixed-length utterance embedding into a plurality of corresponding fixed-length mel-frequency spectrogram frames representing the corresponding prosody of the reference audio signal. 